A Non-Contact Speech Enhancement Algorithm Based on Wavelet Packet Adaptive Threshold

2012 ◽  
Vol 241-244 ◽  
pp. 194-198
Author(s):  
Hui Jun Xue ◽  
Sheng Li ◽  
Teng Jiao ◽  
Guo Hua Lu ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Speech Signal ◽  
Wavelet Packet ◽  
Wiener Filter ◽  
Adaptive Threshold ◽  
Human Communication ◽  
Frequency Signal ◽  
Time Frequency ◽  
Detecting Method ◽  
Enhancement Algorithm

Speech is an important method for human communication. In this paper, we developed a new method for detecting speech signal. Because of the advantage of this speech detecting method, it has great potential application value in many fields. Simultaneously, basing on the good capability of wavelet packet for analyzing time-frequency signal, this paper also developed an algorithm of wavelet packet threshold by using hard threshold and soft threshold for removing noise. Comparing to spectral subtraction and Wiener filter speech enhancement algorithm, the proposed algorithm takes on a better performance on noise removing and speech signal reserving.

An Non-Contact Speech Enhancement Algorithm Based on Lifting Scheme

2014 ◽  
Vol 513-517 ◽  
pp. 3813-3817
Author(s):  
Hui Jun Xue ◽  
Sheng Li ◽  
Teng Jiao ◽  
Yang Zhang ◽  
Hao Lv ◽  
...  
Keyword(s):  
Speech Signal ◽  
Traditional Method ◽  
Wiener Filter ◽  
Lifting Scheme ◽  
The Body ◽  
Good Time ◽  
Human Beings ◽  
Speech Detection ◽  
Time Frequency ◽  
Enhancement Algorithm

The traditional method for detecting speech signal needs the help of microphone, which must be placed closely to the body of human beings. To some extent, this method would bring several inconveniences. Non-contact speech detection method breaks through the limitation of the traditional method, this new kind of speech obtaining method can detect speech signal quite well even in strong noisy background. However, this non-contact speech detecting system also produces some electromagnetic noise and circuit noise, which reduced the quality of radar speech signal. Therefore, based on the good time-frequency analyze performance, the lifting scheme was also proposed in this paper to remove noise from radar speech. Comparing to classical enhancement algorithm, such as spectral subtraction and Wiener filter, the proposed algorithm can remove the component of noise availably and reserve the original pure speech signal in a promising way.

The Energy Distribution of Tire-Road Noise and Noisy Speech Based on Wavelet Packet

2011 ◽  
Vol 464 ◽  
pp. 721-724 ◽  
Author(s):  
Zhi Yong He ◽  
Li Heng Luo
Keyword(s):  
Energy Distribution ◽  
Speech Enhancement ◽  
Mobile Communications ◽  
Wavelet Packet ◽  
Noise Signal ◽  
Wavelet Packets ◽  
Road Noise ◽  
Noisy Speech ◽  
Time Frequency ◽  
The Difference

Speech enhancement is very important for mobile communications or some other applications in car. The energy distribution of signal is the basis of algorithms which denoise noisy speech in time-frequency domain. In this work, the noise regarded is the tire-road noise when driving in expressway. Wavelet packets transform is used in the analysis. After decomposing noise signal and noisy speech signal by wavelet packet transform, the analysis for the difference of the energy distribution between noisy speech and noise is finished.

scholarly journals Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors

2016 ◽  
Vol 41 (3) ◽  
pp. 579-590 ◽  
Author(s):  
Pengfei Sun ◽  
Jun Qin
Keyword(s):  
Speech Enhancement ◽  
Wavelet Packet ◽  
Minimum Mean Square Error ◽  
Wavelet Packet Transform ◽  
Perceptual Quality ◽  
Two Dimensional ◽  
Noisy Environments ◽  
Time Frequency ◽  
Generalized Gamma ◽  
Speech Model

Abstract Despite various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially in low signalto-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager-energyoperators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noises (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW).

scholarly journals Speech enhancement methods based on binaural cue coding

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xianyun Wang ◽  
Changchun Bao
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Channel Correlation ◽  
Noisy Speech ◽  
Time Frequency ◽  
Improve Accuracy ◽  
Better Than

AbstractAccording to the encoding and decoding mechanism of binaural cue coding (BCC), in this paper, the speech and noise are considered as left channel signal and right channel signal of the BCC framework, respectively. Subsequently, the speech signal is estimated from noisy speech when the inter-channel level difference (ICLD) and inter-channel correlation (ICC) between speech and noise are given. In this paper, exact inter-channel cues and the pre-enhanced inter-channel cues are used for speech restoration. The exact inter-channel cues are extracted from clean speech and noise, and the pre-enhanced inter-channel cues are extracted from the pre-enhanced speech and estimated noise. After that, they are combined one by one to form a codebook. Once the pre-enhanced cues are extracted from noisy speech, the exact cues are estimated by a mapping between the pre-enhanced cues and a prior codebook. Next, the estimated exact cues are used to obtain a time-frequency (T-F) mask for enhancing noisy speech based on the decoding of BCC. In addition, in order to further improve accuracy of the T-F mask based on the inter-channel cues, the deep neural network (DNN)-based method is proposed to learn the mapping relationship between input features of noisy speech and the T-F masks. Experimental results show that the codebook-driven method can achieve better performance than conventional methods, and the DNN-based method performs better than the codebook-driven method.

Speech Enhancement based on Wiener Filter and Compressive Sensing

2016 ◽  
Vol 2 (2) ◽  
pp. 367
Author(s):  
Amart Sulong ◽  
Teddy Surya Gunawan ◽  
Mira Kartiwi
Keyword(s):  
Compressive Sensing ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Algorithm Design ◽  
Wiener Filter ◽  
Noise Signal ◽  
High Signal ◽  
Noisy Speech ◽  
Performance Results

<p><em>In communication medium to satisfy the speech enhancement process by using differents methodologies and algoirthms are the key term in testing the system design well enough to produce the best performance results for the speech system. The Wiener filter is one of the classical algorithm that applied to speech process to avoid the noise attacking the speech signal. In other word, compressive sensing method by randomize measurement matrix are combined with the Wiener filter to analyse the noisy speech signal with less introduce to noise signal and producing high signal to noise ratio. The PESQ is used to measure the quality of the proposed algorithm design. As in the experimental results shows that, attacking of defferent noise environments in speech signal still effectively improve the performance of noisy speech with maintain the high score of the PESQ quality. </em><em></em></p>

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